Abstract: The disclosed systems and methods are generally directed to interpreting neuromuscular signals. The system includes (A) a plurality of neuromuscular sensors that detect a plurality of neuromuscular signals from a user and (B) at least one multiplexer in communication with the plurality of neuromuscular sensors and that is capable of dynamically adjusting neuromuscular sensor processing based on neuromuscular signal characteristics. A computer processor is programmed to (i) receive a set of neuromuscular signals from the plurality of neuromuscular sensors, (ii) determine, via a real-time system, at least one signal characteristic included in a neuromuscular signal, where the neuromuscular signal is associated with a first neuromuscular sensor included in the plurality of neuromuscular sensors; and (iii) dynamically reconfigure the processing of neuromuscular signals from the plurality of neuromuscular sensors based on an output from the multiplexer.

Abstract: Computerized systems, methods, and computer-readable storage media storing code for the methods enable feedback to be provided to a user based on neuromuscular signals sensed from the user. One such system includes neuromuscular sensors and at least one computer processor. The sensors, which are arranged on one or more wearable devices, are configured to sense neuromuscular signals from the user. The at least one computer processor is or are programmed to process the neuromuscular signals using one or more inference models, and to provide feedback to the user based on one or both of: the processed neuromuscular signals and information derived from the processed neuromuscular signals. The feedback includes visual feedback of information relating to one or both of: a timing of an activation of at least one motor unit of the user and an intensity of the activation of the at least one motor unit of the user.

Abstract: Methods and apparatus for controlling a physical object in an environment based, at least in part, on neuromuscular signals. The method comprises recording a plurality of neuromuscular signals from a plurality of neuromuscular sensors arranged on one or more wearable devices worn by a user, receiving a selection of a physical object within the environment, and controlling, based at least in part on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, an operation of the selected object within the environment.

Abstract: Methods and apparatus for controlling a physical object in an environment based, at least in part, on neuromuscular signals. The method comprises recording a plurality of neuromuscular signals from a plurality of neuromuscular sensors arranged on one or more wearable devices worn by a user, receiving a selection of a physical object within the environment, and controlling, based at least in part on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, an operation of the selected object within the environment.

Abstract: Computerized systems, methods, kits, and computer-readable storage media storing code for implementing the methods are provided for controlling an extended reality (XR) system. One such system includes: one or more neuromuscular sensors that sense neuromuscular signals from a user, and at least one computer processor. The neuromuscular sensor(s) is or are arranged on one or more wearable devices structured to be worn by the user to sense the neuromuscular signals. The at least one computer processor is or are programmed to: identify a first muscular activation state of the user based on the neuromuscular signals; determine, based on the first muscular activation state, an operation of an XR system to be controlled; identify a second muscular activation state of the user based on the neuromuscular signals; and output, based on the second muscular activation state, a control signal to the XR system to control the operation of the XR system.

Abstract: Methods and apparatus for mitigating neuromuscular signal artifacts are described. The method comprises detecting in real-time, by at least one computer processor, one or more artifacts in a plurality of neuromuscular signals recorded by a plurality of neuromuscular sensors, determining, based at least in part, on the detected one or more artifacts, a plurality of derived neuromuscular signals to mitigate the one or more artifacts, and providing, as input to one or more trained statistical models, the plurality of derived neuromuscular signals.